• Nice to keep in touch, hear what’s happening
• Fun to learn stuff from and with others
• Have a spot (physical & online) to ask for ideas, help, direction
• Food
• Ask each
• Total work-in-progress, will evolve continuously, so ideas welcome!
• solescodeRs.github.io
• Slack

• Understand the principles and importance of reproducibility in science
• Learn the key steps in producing reproducible research
• Create and detail the structure and value of ‘tidy’ projects, data, and code

Reproducible:

Replicable:

Reproducible: The same result can be independently reached given the same data & analysis pipeline.

Replicable: The same result can be independently reached given independent data & analysis pipeline.

Huge practical benefits!

• Easier to share and reuse, as projects are richly documented and detailed, which is the point of science!
• Newly-collected data can be integrated easily into existing projects and analyses
• Mistakes are easier to detect and remedy
• Documents (e.g. manuscripts) are easier to revise & update as data and/or analyses change
• The exact steps you took to produce an end-result (e.g. a manuscript) will be richly documented and self-contained forever & ever
• You can more easily steal from yourself & minimise the duplication of effort into the future
• Easier to collaborate (once everyone’s on-board)

• Principle 1: Someone unfamiliar with your project should be able to look at your computer files and understand in detail what you did and why. (N.B ‘Someone’ includes future-you).

• Principle 2: Everything you do, you will probably have to do over again.

Source: phil_g

• informative_project_name
  • README.txt (a readme text file at the top-level of the directory which outlines the broad structure/details of the project)
  • /data (raw data, such as images or videos, as well as the processed products for analysis)
  • /doc (all notes and the draft manuscript associated with the project)
  • /figs (figures to be included in the manuscript, typically generated via code)
  • /output (programmatically-generated output from data handling and analysis such as tables of statistical results, which can be re-generated at any time)
  • /R (code for processing and analysing data)

1. Each variable forms a column, each observation a row
2. Use plain text
3. Choose good names
4. No empty cells
5. Use metadata
6. Treat raw data as read-only
7. Be consistent
8. Dates are awful

Messy

Source: Wickham (2009) & Pew Research Center

Tidy

Source: Wickham (2009) & Pew Research Center

Messy

Source: Wickham (2009)

Tidy

Source: Wickham (2009)

Untidy

• myabstract.docx
• Tom’s best ideas.docx
• figure 1.png
• newNEWv2_dontdelete_forREAL_dont_FINALfinal_v2.xlsx

Tidy

• 2020_abstract_for_hons_conf.docx
• toms_ideas.docx
• fig_01_scatterplot_length_width.png
• 2019-08-07_raw_data_LIFE4000.xlsx

Good names are

• Machine readable
• Human readable
• Nicely ordered

Good names are

• Machine readable
  • No special characters or formatting

Good names are

• Machine readable
  • No special characters or formatting

Good names are

• Machine readable
• Human readable
  • Names contain information on content

Good names are

• Machine readable
• Human readable
  • Names contain information on content

Good names are

• Machine readable
• Human readable
• Nicely ordered
  • Think about sorting

Good names are

• Machine readable
• Human readable
• Nicely ordered
  • Think about sorting

Or special characters

##    cow_ID milk_volume weight
## 1     moo          12   1100
## 2   bumbo           2   1201
## 3    spot           ?   1084
## 4 jeffrey               1044
## 5    holy          16   1244
## 6   daisy           -   1093

Use NA if NA, or 0 if 0

##    cow_ID milk_volume weight
## 1     moo          12   1100
## 2   bumbo           2   1201
## 3    spot          NA   1084
## 4 jeffrey           0   1044
## 5    holy          16   1244
## 6   daisy           0   1093

or a ‘data dictionary’

or a ‘data dictionary’

• Metadata = data about data
• A file describing the contents & structure of a separate file
• The richer & more detailed the better
• Essential to reproducibility (not least for yourself)

Hands off!

Modify by hand (only when unavoidable)

• Create a work on a copy
• Document every change you make in a separate file

Modify via code (whenever possible)

e.g. Naming conventions

• snake_case
• camelCase
• SCREAMING_SNAKE_CASE
• kebab-case
• Train-Case

• library(janitor)
  • clean_names(): creates consistent, tidy-rule-following variable names
    
  • remove_empty(): remove rows/columns/both containing missing or empty data
  • convert_to_date(): take the fight to Excel’s concept of a date
• library(tidyverse)
  • Set of ~25 packages for cleaning/wrangling/visualising…
  • tidyr: reshaping data
  • dplyr: manipulating data

1. Choose good names & be consistent
2. Write human-readable code
3. Keep it self-contained
4. Keep it well-styled (and use help)

Good:

dat_heights_2020 <- read.csv('2020_field_data_heights.csv')

Less good (maybe)

dat_field <- read.csv('2020_field_data_heights.csv')

Bad

dat <- read.csv('2020_field_data_heights.csv')

## ----------------- Load data ----------------- ##

`dat_heights_2020 <- read.csv('2020_field_data_heights.csv')`  # Summer 2020
`dat_heights_2021 <- read.csv('2021_field_data_heights.csv')`  # Winter 2021

## ----------------- Summarise data ----------------- ##

# Calculate mean +- SD heights
dat_heights_summary %>% 
  summarise(mean = mean(),
            sd = sd(),
            n = n())

Good

height <- cm * 6 + mm
mean(x, na.rm = TRUE)

Bad

height<-cm*6+mm
mean(x,na.rm=TRUE)

Good

do_something_very_complicated(
  something = "that",
  requires = many,
  arguments = "which may be long"
)

Bad

do_something_very_complicated("that", requires, many, arguments, "which may be long")

• Forget setwd() exists
• Assume a script is being run from the ‘root’ of the project
• Use paths relative to that

Bad

`data <- read.csv('C:/tomscomputer/projects/feeding_experiment/data/feeding_data.csv')`

• Won’t run on any other computer
• Won’t run on my computer, if I ever move it or modify my filesystem

Good

`data <- read.csv('data/feeding_data.csv')`

Also see here::here()

• Understand the principles and importance of reproducibility in science
• Learn the key steps in producing reproducible research
• Create and detail the structure and value of ‘tidy’ projects, data, and code